Type:	Package
Title:	Small Area Estimation with Cluster Information for Estimation of Non-Sampled Areas
Version:	0.1.2
Description:	Implementation of small area estimation (Fay-Herriot model) with EBLUP (Empirical Best Linear Unbiased Prediction) Approach for non-sampled area estimation by adding cluster information and assuming that there are similarities among particular areas. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Anisa et al. (2013) <doi:10.9790/5728-10121519>.
License:	MIT + file LICENSE
URL:	https://github.com/Alfrzlp/sae-ns
BugReports:	https://github.com/Alfrzlp/sae-ns/issues
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 4.00)
RoxygenNote:	7.2.0
Imports:	cli, dplyr, ggplot2, methods, rlang, stats, tidyr
NeedsCompilation:	no
Packaged:	2024-11-18 01:40:35 UTC; alfrz
Author:	Ridson Al Farizal P [aut, cre, cph], Azka Ubaidillah [aut]
Maintainer:	Ridson Al Farizal P <alfrzlp@gmail.com>
Repository:	CRAN
Date/Publication:	2024-11-18 04:40:03 UTC

Akaike's An Information Criterion.

Description

Generic function calculating Akaike's "An Information Criterion" for EBLUP model

Usage

## S3 method for class 'eblupres'
AIC(object, ...)

## S3 method for class 'eblupres'
BIC(object, ...)

Arguments

Value

AIC value.

Examples

m1 <- eblupfh_cluster(y ~ x1 + x2 + x3, data = mys, vardir = "var", cluster = "clust")
AIC(m1)

Create a complete ggplot appropriate to a particular data type

Description

autoplot() uses ggplot2 to draw a particular plot for an object of a particular class in a single command. This defines the S3 generic that other classes and packages can extend.

Usage

autoplot(object, ...)

Arguments

Value

a ggplot object

See Also

autolayer(), ggplot() and fortify()

Autoplot EBLUP results.

Description

Autoplot EBLUP results.

Usage

## S3 method for class 'eblupres'
autoplot(object, variable = "RSE", ...)

Arguments

Value

plot.

Examples

library(saens)

m1 <- eblupfh_cluster(y ~ x1 + x2 + x3, data = mys, vardir = "var", cluster = "clust")
autoplot(m1)

Extract Model Coefficients.

Description

Extract Model Coefficients.

Usage

## S3 method for class 'eblupres'
coef(object, ...)

Arguments

Value

model coefficients

Examples

m1 <- eblupfh_cluster(y ~ x1 + x2 + x3, data = mys, vardir = "var", cluster = "clust")
coef(m1)

EBLUPs based on a Fay-Herriot Model.

Description

This function gives the Empirical Best Linear Unbiased Prediction (EBLUP) or Empirical Best (EB) predictor under normality based on a Fay-Herriot model.

Usage

eblupfh(
  formula,
  data,
  vardir,
  method = "REML",
  maxiter = 100,
  precision = 1e-04,
  scale = FALSE,
  print_result = TRUE
)

Arguments

Details

The model has a form that is response ~ auxiliary variables. where numeric type response variables can contain NA. When the response variable contains NA it will be estimated with cluster information.

Value

The function returns a list with the following objects (df_res and fit): df_res a data frame that contains the following columns:

• y variable response
  

• eblup estimated results for each area
  

• random_effect random effect for each area
  

• vardir variance sampling from the direct estimator for each area
  

• mse Mean Square Error
  

• rse Relative Standart Error (%)
  

fit a list containing the following objects:

• estcoef a data frame with the estimated model coefficients in the first column (beta), their asymptotic standard errors in the second column (std.error), the t-statistics in the third column (tvalue) and the p-values of the significance of each coefficient in last column (pvalue)
  

• model_formula model formula applied
  

• method type of fitting method applied (ML or REML)
  

• random_effect_var estimated random effect variance
  

• convergence logical value that indicates the Fisher-scoring algorithm has converged or not
  

• n_iter number of iterations performed by the Fisher-scoring algorithm.
  

• goodness vector containing several goodness-of-fit measures: loglikehood, AIC, and BIC
  

References

1. Rao, J. N., & Molina, I. (2015). Small area estimation. John Wiley & Sons.

Examples

library(saens)

m1 <- eblupfh(y ~ x1 + x2 + x3, data = na.omit(mys), vardir = "var")
m1 <- eblupfh(y ~ x1 + x2 + x3, data = na.omit(mys), vardir = ~var)

EBLUPs based on a Fay-Herriot Model with Cluster Information.

Description

This function gives the Empirical Best Linear Unbiased Prediction (EBLUP) or Empirical Best (EB) predictor based on a Fay-Herriot model with cluster information for non-sampled areas.

Usage

eblupfh_cluster(
  formula,
  data,
  vardir,
  cluster,
  method = "REML",
  mse_method = "jackknife",
  maxiter = 100,
  precision = 1e-04,
  scale = FALSE,
  print_result = TRUE
)

Arguments

Details

The model has a form that is response ~ auxiliary variables. where numeric type response variables can contain NA. When the response variable contains NA it will be estimated with cluster information.

Value

The function returns a list with the following objects df_res and fit: df_res a data frame that contains the following columns:

• y variable response
  

• eblup estimated results for each area
  

• random_effect random effect for each area
  

• vardir variance sampling from the direct estimator for each area
  

• mse Mean Square Error
  

• cluster cluster information for each area
  

• rse Relative Standart Error (%)
  

fit a list containing the following objects:

• estcoef a data frame with the estimated model coefficients in the first column (beta), their asymptotic standard errors in the second column (std.error), the t-statistics in the third column (tvalue) and the p-values of the significance of each coefficient in last column (pvalue)
  

• model_formula model formula applied
  

• method type of fitting method applied (ML or REML)
  

• random_effect_var estimated random effect variance
  

• convergence logical value that indicates the Fisher-scoring algorithm has converged or not
  

• n_iter number of iterations performed by the Fisher-scoring algorithm.
  

• goodness vector containing several goodness-of-fit measures: loglikehood, AIC, and BIC
  

References

1. Rao, J. N., & Molina, I. (2015). Small area estimation. John Wiley & Sons.

2. Anisa, R., Kurnia, A., & Indahwati, I. (2013). Cluster information of non-sampled area in small area estimation. E-Prosiding Internasional| Departemen Statistika FMIPA Universitas Padjadjaran, 1(1), 69-76.

Examples

library(saens)

m1 <- eblupfh_cluster(y ~ x1 + x2 + x3, data = mys, vardir = "var", cluster = "clust")
m1 <- eblupfh_cluster(y ~ x1 + x2 + x3, data = mys, vardir = ~var, cluster = ~clust)

Synthetic Estimator.

Description

Synthetic estimator is one of the simple methods to obtain predicted values of mean specific area parameters, which the direct estimates are unknown. Based on estimated of parameter coefficient models using Empirical Best Unbiased Prediction (EBLUP), the synthetic estimator is obtained by calibrating the estimated parameter coefficient to the auxiliary variables.

Usage

eblupfh_ns(
  formula,
  data,
  vardir,
  method = "REML",
  maxiter = 100,
  precision = 1e-04,
  scale = FALSE,
  print_result = TRUE
)

Arguments

Details

The model is defined as response ~ auxiliary variables, where the response variable, of numeric type, may contain NA values. When the response variable contains NA, it will be estimated using a synthetic estimator.

Value

The function returns a list with the following objects df_res and fit: df_res a data frame that contains the following columns:

• y variable response
  

• eblup estimated results for each area
  

• random_effect random effect for each area
  

• vardir variance sampling from the direct estimator for each area
  

• mse Mean Square Error
  

• cluster cluster information for each area
  

• rse Relative Standart Error (%)
  

fit a list containing the following objects:

• estcoef a data frame with the estimated model coefficients in the first column (beta), their asymptotic standard errors in the second column (std.error), the t-statistics in the third column (tvalue) and the p-values of the significance of each coefficient in last column (pvalue)
  

• model_formula model formula applied
  

• method type of fitting method applied (ML or REML)
  

• random_effect_var estimated random effect variance
  

• convergence logical value that indicates the Fisher-scoring algorithm has converged or not
  

• n_iter number of iterations performed by the Fisher-scoring algorithm.
  

• goodness vector containing several goodness-of-fit measures: loglikehood, AIC, and BIC
  

References

1. Rao, J. N., & Molina, I. (2015). Small area estimation. John Wiley & Sons.

Examples

library(saens)

m1 <- eblupfh_ns(y ~ x1 + x2 + x3, data = mys, vardir = "var")
m1 <- eblupfh_ns(y ~ x1 + x2 + x3, data = mys, vardir = ~var)

Extract Log-Likelihood.

Description

Extract Log-Likelihood.

Usage

## S3 method for class 'eblupres'
logLik(object, ...)

Arguments

Value

Log-Likehood value

Examples

library(saens)

model1 <- eblupfh_cluster(y ~ x1 + x2 + x3, data = mys, vardir = "var", cluster = "clust")
logLik(model1)

mys: mean years of schooling people with disabilities in Papua Island, Indonesia.

Description

A dataset containing the mean years of schooling people with disabilities in Papua Island, Indonesia in 2021.

Usage

mys

Format

A data frame with 42 rows and 7 variables with 10 domains are non-sampled areas.

area

regency municipality

y

mean years of schooling people with disabilities

var

variance sampling from the direct estimator for each area

rse

relative standard error (%)

x1

Number of Elementary Schools

x2

Number of Junior High Schools

x3

Number of Senior High Schools

clust

Cluster

n

Number of eligible samples

weight

Weight

Source

https://www.bps.go.id

Summarizing EBLUP Model Fits.

Description

'summary' method for class "eblupres".

Usage

## S3 method for class 'eblupres'
summary(object, ...)

Arguments

Value

The function returns a data frame that contains the following columns:
* y variable response
* eblup estimated results for each area
* random_effect random effect for each area
* vardir variance sampling from the direct estimator for each area
* mse Mean Square Error
* cluster cluster information for each area
* rse Relative Standart Error (

Examples

library(saens)

model1 <- eblupfh_cluster(y ~ x1 + x2 + x3, data = mys, vardir = "var", cluster = "clust")
summary(model1)